UniExo: Unified Multi-Skill Policies for Musculoskeletal Locomotion and Co-Adaptive Exoskeleton Control
cs.RO, cs.LG
Submitted: 2026-09-17
Updated: 2026-09-17
Comments: 9 pages, 8 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Exo-Plore: Exploring Exoskeleton Control Space through Human-aligned Simulation
- Staged Multi-Agent Training (SMAT) for Hip Exoskeletons: Metabolic and Biomechanical Validation of a Simulation-Trained Co-Adaptive Controller
- MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control
- SMAT: Staged Multi-Agent Training for Co-Adaptive Exoskeleton Control
- KINESIS: Motion Imitation for Human Musculoskeletal Locomotion
- Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale
- mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning
- LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion
- Proximal Policy Optimization Algorithms
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving